We experiment with three models applied over the aforementioned data. Firstly, we use SEGAN in a plain adversarial setup with the LSGAN loss as in Eq. REF , where the only signal comes from \(\texttt {real}\)  or \(\texttt {fake}\)  decisions. This is the baseline, which is trained for 400 epochs with two-timescale update rule (TTUR) learning rates [1]} \(\eta _D = 4\cdot 10^{-4}\)  and \(\eta _G = 1\cdot 10^{-4}\) . Secondly, we apply the acoustic losses presented in Sec. REF . This system trains also for 400 epochs, but the learning rates are balanced equal \(\eta _D = \eta _G = 5\cdot 10^{-5}\) , because the discriminator already has an advantage with the extra signals and TTUR involved a noisier result on \(G\) . We name this approach SEGAN-Aco. Finally, we include our improved proposal, for which we pre-train the system over 100 epochs the same way we do with the baseline, and then we activate the acoustic losses for the remaining 300 epochs, also lowering the learning rates to \(\eta _D = \eta _G = 5\cdot 10^{-5}\) . We name this approach SEGAN-PTAco.
